A Hybrid Soft Computing and Deep Learning Framework for Modeling Diagnostic Uncertainty in Prostate Cancer Grading

Abstract

Background: Ensemble methods such as Extreme Gradient Boosting (XGBoost) and deep learning models including Multi-Layer Perceptrons (MLPs) can attain high predictive accuracy but handling uncertainty and enhancing resilience are still critical issues. Hybrid soft computing methods can improve the stability and judgment reliability in difficult classification problems.

Methods: This approach combines an optimised MLP and XGBoost model, in addition to some uncertainty-based techniques, to create a novel architecture. During the preprocessing of data, treatment of missing values, normalization, and stratified train-test splits were performed. The MLP architecture was built by configuring dropout regularization (dropout1 = 0.3, dropout2 = 0.2), a learning rate of 0.001, and with hidden layers of 256 and 64. The XGBoost model was configured with n_estimators = 200, max_depth = 3, learning_rate = 0.05, subsample = 0.8, and colsample_bytree = 0.8. The different combinations of predictors induced a generalization performance improvement, and therefore, my hybrid decision-making method was built.

Results: The optimised hybrid model provided the highest accuracy of 0.998, which its good prediction ability. Combining gradient boosting and deep learning reduced overfitting and increased classification stability. Dropout regulation and controlled tree depth helped to increase generation. Comparative analysis showed better performance than the standalone baseline setting.

Conclusion: The suggested framework for hybrid soft computing significantly improves the accuracy and resilience of the predictions. The framework provides a robust and scalable approach for high-precision classification tasks by integrating optimised MLP and XGBoost models with systematic hyperparameter tuning. This method is especially helpful in applications demanding highly accurate and reliable decisions.